Explaining microbial scaling laws using Bayesian inference

In this project, we combined methods from Statistical Physics and Bayesian Data Analysis to elucidate the principles behind cellular growth and division. We studied various classes of individual-based growth-division models and inferred individual-level processes (model structures and likely ranges of associated parameters) from sigle-cell observations.

In the Bayesian framework, we formalized our process understanding the form of different rate functions, expressing the dependence of growth and division rates on variables characterizing the cell’s state (such as size and protein content). We calculated the Bayesian posteriors for the parameters of these functions and performed a model comparison to determine which was more consistent with the data coming from experimental observations.